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funding availability and satisfactory performance. Applicants should possess a Ph.D. degree with a specialisation in Psychology and Computational Modelling/advanced neuroimaging data processing skills/big
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Computational Modelling/advanced neuroimaging data processing skills/big data processing techniques. They should have a strong interest in experimental research and demonstrated scientific expertise and excellent
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advanced research in structural health monitoring, including hardware development. Perform numerical studies and model testing. Analyze and interpret data to provide recommendations for system design and
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self-driven, highly motivated, creative with excellent communication skills in written and spoken English and Cantonese. Expertise and knowledge in AI deep learning model development on histology whole
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analyses, multilevel modelling, data visualisation, and state-of-the-art statistical and epidemiological models would be an advantage. The appointees will be primarily responsible for applied and
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of responsibility and commitment. Preference will be given to those with previous research experience in virus-host interaction, influenza viruses and animal models. Eligibility to work in Biosafety
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highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences across semiconductor manufacturing tools, with the objectives of reducing cycle times
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or virology; and preferably with basic laboratory skills of animal studies (mouse models). The appointees will conduct research projects related to influenza virus, coronavirus, or Epstein-Barr virus under
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. Expertise and knowledge in AI deep learning model development on histology whole slide imaging analysis in computational pathology is essential. Applicants should have a solid publication record and
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wafers are processed across hundreds or even thousands of manufacturing tools following highly complex workflows. We aim to develop optimization models and algorithms to improve wafer processing sequences